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About
Pramod Kumar Voola is an Engineering Manager with extensive experience in Life Sciences Clinical Trials, specializing in data engineering, automation testing, and AI. Based in New York, he has a proven track record in managing critical healthcare applications like eCOA platforms, enhancing patient safety and data accuracy. With skills in leadership, ethical AI advocacy, and mobile automation, he excels in driving innovations and managing high-performing teams. Pramod is currently pursuing a Data Science Certification from MIT and holds an AWS Cloud Practitioner credential. His career includes notable roles at Medidata Solutions, Capital One, and Cognizant Technology Solutions.
Pramod Kumar Voola is a results-driven Engineering Manager with extensive experience in the Life Sciences Clinical Trials industry, demonstrating expertise in leading high-performing teams in Data Engineering, Test Engineering, and Automation Testing to develop and deploy innovative solutions that enhance patient safety, compliance, and data accuracy. With a deep-rooted understanding of healthcare applications, he has played a pivotal role in managing critical eCOA (electronic Clinical Outcome Assessment) platforms, contributing to advancements in medical research and public health. Currently, he is leveraging AI and data-driven technologies to drive transformative innovations that impact healthcare outcomes for various clients. Pramod excels in leadership and people management, fostering team growth and empowerment while aligning individual goals with organizational objectives. His technical leadership extends to spearheading GenAI initiatives, including clinician-based chatbots designed to improve patient-clinician interactions. An advocate for responsible AI, he actively contributes to his organization’s Ethical AI group, ensuring the development and deployment of AI solutions adhere to ethical standards. He has led mobile automation efforts, streamlining testing strategies and increasing efficiency, while also effectively managing conflicts within cross-functional teams through collaboration and communication. His expertise in data engineering includes building scalable data streaming pipelines using technologies like Airflow, Snowpark, Iceberg, and Apache Spark to ensure data quality and compliance. With an ongoing pursuit of a Data Science Certification from MIT, Pramod has expanded his proficiency in Python, data visualization, and machine learning techniques, equipping himself with advanced analytical skills. His professional journey includes his current role at Medidata Solutions, where he has led the development of eCOA compliance reports, architected data streaming solutions, and spearheaded AI-driven capabilities to support clinical decision-making. Prior to this, he served as a Principal Associate at Capital One Financial Services, where he was responsible for designing and implementing automation testing strategies, performance testing, defect management, and risk assessment, ensuring adherence to industry regulations. During his tenure at Cognizant Technology Solutions, he managed automation testing for banking applications, optimized testing frameworks, led teams, and maintained strong client relationships. His earlier roles at Ness Technologies involved leading testing efforts for Standard & Poor’s Ratings application, implementing automation testing using TOSCA, performing API testing, database validation, and enhancing application performance through performance testing. He also played a crucial role in coordinating nearshore and offshore teams, ensuring seamless execution of projects. As a Programmer at Cognizant Technology Solutions, he worked on the FACETS product for healthcare claims processing and enrollment, focusing on requirement analysis, manual testing, and defect management. His educational background includes a Bachelor of Science in Computer Science from Osmania University, complemented by certifications such as AWS Cloud Practitioner and multiple LinkedIn Learning certifications in Python, Data Science, and Machine Learning. With an exceptional ability to navigate complex technical landscapes, mentor teams, and drive AI-led innovations, Pramod remains at the forefront of engineering leadership, shaping the future of data-driven healthcare solutions while continuously enhancing his expertise in AI, cloud computing, and automation to make meaningful contributions to the industry.
Skills & Expertise
machine learning
Machine Learning
Clinical Data Analytics
Artificial Intelligence
Sentiment Analysis
Test Automation
Software Development
Risk assessment
Machine Learning Techniques
Natural Language Processing (NLP)
Reporting
Testing
Snowflake
NLP
Text Classification
Object-Oriented Programming
Defect Management
Software Architecture
Patient Safety
Generative AI
Large Language Models
Data Streaming
Mobile Automation
Mobile Automation
Test Driven Development
eCOA Platforms
Data Quality
Clinical Decision-Making
Strong Client Relationships
Large Language Models (LLM)
Generative AI
Amazon Q
Amazon Bedrock
Amazon SageMaker
KILT
Spark NLP
PySpark
Data Analysis
Apache Flink
Apache Airflow
Snowflake
Data Streaming
Visual Analytics
Research Interests
Computer Science
risk assessment
Patient Safety
Automation Testing
Data Engineering
Medical Research Leadership
Data quality rules
Performance Testing
Life Sciences Clinical Trials
Test Engineering
eCOA Platforms
Data Accuracy
AI-Driven Innovations
GenAI Initiatives
Responsible AI
Data Streaming Pipelines
Data Compliance
Clinical Decision-Making
Automation Strategies
Defect Management
Strong Client Relationships
Connect With Me
Experience
Manager and Testing Engineering
- Strategic Thinking | Technical and Self Driven | A results-oriented engineering manager with 4 years of experience leading and scaling data engineering and automation teams. Proven success in delivering impactful reporting analytics solutions and MVPs for complex products. Continuous Learner in data architecture, road-mapping, and project execution. Passionate about building high-performing teams and mentoring engineers to achieve their best work. Core Competencies Data Engineering: ETL pipelines, data warehousing, data modeling, Python, SQL, PySpark, AWS, Airflow Data Science: Machine Learning Practitioner Automation: Test automation frameworks, CI/CD pipelines Architecture: MFE, Event Driven Leadership: Team building, mentorship, conflict resolution, performance management Project Management: SAFe, Agile methodologies, resource allocation, risk mitigation
Education
Osmania University
Conferences & Seminars (3)
NLP Summit 2024 - Applied Generative AI and Language Processing
The NLP Summit is the gathering place for those putting Generative AI, Natural Language Processing, and Large Language Models to good use. Now in its fourth year, this conference showcases best practices, real-world case studies and challenges in applying these technologies in practice – as well as the latest open-source libraries, tools, and models you can put to use today. The NLP Summit brings together the growing community interested in building Generative AI applications in healthcare, life science, finance, eCommerce, media, recruiting, and more.
Applied Generative AI for Data Scientists
This two-day workshop will walk you through building state-of-the-art Generative AI and Natural Language Processing (NLP) using John Snow Labs’ open-source libraries. This is a hands-on workshop for data scientists that will enable you to write and run live Python notebooks that put the technology to work.
The first day covers the open-source Spark NLP library for information extraction at scale - including reusing, training, and combining AI models for tasks like named entity recognition, text classification, spelling & grammar correction, question answering, knowledge extraction, sentiment analysis, and more. The second day focuses on libraries and integrations specifically for preparing data for RAG LLM solutions, including document splitting, cleaning, metadata enrichment, summarization, and embeddings calculation.
The workshop is organized in two four-hour-long sessions, each followed by self-guided coding, on Python notebooks relevant to each section. This is a live online workshop whose instructors are current lead contributors to the John Snow Labs open-source codebase.
InfoQ Dev Summit Boston
Two days of actionable advice on critical dev priorities from trusted, active senior software developers.
Topics include: Generative AI, Software Supply Chain Security,
Scalable Architectures, and more.
InfoQ Dev Summit provides senior developers with the insights they need to master today's development hurdles and critical decisions. Learn from senior developers facing the same challenges as you as they share proven tactics, not just trends, empowering you to make smart, focused choices for your immediate dev roadmap.
Certificates & Licenses (9)
Course of Certification
LangChain for AI
The holder of this badge has completed the following course by Pearson. The holder of this badge is capable of exploring different LLM APIs, understanding the LangChain framework, and building LLM applications using LangChain without requiring in-depth expertise in ML.
Spark NLP for Data Scientists
This course will walk you through building state-of-the-art natural language processing (NLP) solutions using John Snow Labs’ open-source Spark NLP library. Our library consists of more than 20,000 pretrained models with 250 plus languages. This is a course for data scientists that will enable you to write and run live Python notebooks that cover the majority of the open-source library’s functionality. This includes reusing, training, and combining models for NLP tasks like named entity recognition, text classification, spelling & grammar correction, question answering, knowledge extraction, sentiment analysis and more.
The course is divided into 11 sections: Text Processing, Information Extraction, Dependency Parsing, Text Representation with Embeddings, Sentiment Analysis, Text Classification, Named Entity Recognition, Question Answering, Multilingual NLP, Advanced Topics such as Speech to text recognition, and Utility Tools &Annotators. In addition to video recordings with real code walkthroughs, we also provide sample notebooks to view and experiment. At the end of the cost, you will have an opportunity to take a certification, at no cost to you.
Generative AI with Large Language Models
- Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
- Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
- Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements
- Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project
- Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners
Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.
Data Scientist's Toolbox
The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio
Fundamentals of Generative AI for Beginners
Generative AI is a disruptive technology and its impact has been immense. It is causing businesses to rethink customer strategies and redesign their products, chipmakers are scrambling to keep up with the increased demand for processors, and in academia, educators are altering learning paths and curriculums in every field of study. The material presented in this course will introduce you to the world of generative AI and uncover some of the ingredients that make up this groundbreaking field. Using basic terms and a few simple examples, we will explain what generative AI is, what makes it work, and help build a foundation of knowledge that takes you to the next step on your learning journey. Throughout the course, you'll learn what large language models, neural networks, training data sets, and prompts are. You'll also become familiar with some tools and processes used to build generative AI applications.
Full Stack Development using Python
How Leaders Drive Results and Resolve Conflict in a Hybrid Workplace
Becoming an effective leader in a traditional work setting can be challenging enough. Being a leader in a hybrid workplace requires an altered skill set. These leaders play many roles: Project manager, coach, mediator, productivity guru, and more to drive results. Conflict in this environment can add another layer of stress and risk to performance. In this course, Marlene Chism teaches leaders how to expand their skills to support employees and overall performance in this dynamic work environment.
Becoming an Ally to All
Role in Research Journals (10)
Editorial Board Member
IJNRD
Editorial Board Member
IJNRD
Assistant Editor
IJNRD
Advisory Member
IJNRD
Editorial Board Member
IJNRD
Publications (1)
Innovative clinical apps that improve patient outcomes and simplify procedures are in high demand in healthcare. In
this field, AI has revolutionised prediction models that speed up clinical applicat...
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